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1. Genetic load determines atrophy in hand cortico-striatal pathways in presymptomatic Huntington's disease

Author

Fan Zhang, Demian Wassermann, Ofer Pasternak, Nikolaos Makris, Peter Savadjiev, Hans J. Johnson, Yi Hong, Lauren J. O'Donnell, Carl-Fredrik Westin, Jean-Paul Vonsattel, Yogesh Rathi, Jane S. Paulsen, Department of Computer Science [Athens GA], University of Georgia [USA], Departments of Psychiatry and Radiology [Boston], Brigham and Women's Hospital [Boston], Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), University of Iowa [Iowa City], Columbia University [New York], Department of Psychiatry [Boston], Massachusetts General Hospital [Boston], Laboratory of Mathematics in Imaging [Boston], Harvard Medical School [Boston] (HMS), Department of Computer Science, University of Georgia, Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay, Computational Imaging of the Central Nervous System ( ATHENA ), Inria Sophia Antipolis - Méditerranée ( CRISAM ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), University of Iowa [Iowa], Harvard Medical School [Boston] ( HMS ), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
Male, Striatum, 030218 nuclear medicine & medical imaging, 0302 clinical medicine, Trinucleotide Repeats, CAG-repeats, Neural Pathways, Isotropic volume fraction, Longitudinal Studies, Aged, 80 and over, Cortico-striatal pathways, Radiological and Ultrasound Technology, Putamen, Motor Cortex, Middle Aged, White Matter, Diffusion Tensor Imaging, Huntington Disease, medicine.anatomical_structure, Neurology, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Genetic Load, Anatomy, Tractography, Adult, Adolescent, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Prodromal Symptoms, Biology, Article, Diffusion MRI, White matter, Young Adult, 03 medical and health sciences, Atrophy, Huntington's disease, [ PHYS.PHYS.PHYS-BIO-PH ] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Fractional anisotropy, medicine, Humans, Radiology, Nuclear Medicine and imaging, Equal contribution Keywords: Prodromal-HD, Aged, medicine.disease, Prodromal-HD, [ SDV.NEU ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurology (clinical), Caudate Nucleus, Neuroscience, 030217 neurology & neurosurgery
Abstract

International audience; Huntington's disease (HD) is an inherited neurodegenerative disorder that causes progressive breakdown of striatal neurons. Standard white matter integrity measures like fractional anisotropy and mean diffusivity derived from diffusion tensor imaging were analyzed in prodromal-HD subjects; however, they studied either a whole brain or specific subcortical white matter structures with connections to cortical motor areas. In this work, we propose a novel analysis of a longitudinal cohort of 243 prodromal-HD individuals and 88 healthy controls who underwent two or more diffusion MRI scans as part of the PREDICT-HD study. We separately trace specific white matter fiber tracts connecting the striatum (caudate and putamen) with four cortical regions corresponding to the hand, face, trunk, and leg motor areas. A multi-tensor tractography algorithm with an isotropic volume fraction compartment allows Hong et al., Genetic load determines hand atrophy in preHD 2 estimating diffusion of fast-moving extra-cellular water in regions containing crossing fibers and provides quantification of a microstructural property related to tissue atrophy. The tissue atrophy rate is separately analyzed in eight cortico-striatal pathways as a function of CAG-repeats (genetic load) by statistically regressing out age effect from our cohort. The results demonstrate a statistically significant increase in isotropic volume fraction (atrophy) bilaterally in hand fiber connections to the putamen with increasing CAG-repeats, which connects the genetic abnormality (CAG-repeats) to an imaging-based microstructural marker of tissue integrity in specific white matter pathways in HD. Isotropic volume fraction measures in eight cortico-striatal pathways are also correlated significantly with total motor scores and diagnostic confidence levels, providing evidence of their relevance to HD clinical presentation.

Published
2018
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2. Freewater estimatoR using iNtErpolated iniTialization (FERNET): Characterizing peritumoral edema using clinically feasible diffusion MRI data

Author

Drew Parker, Wes Hodges, Simon Alexander, Ragini Verma, Steven Brem, Ofer Pasternak, Emmanuel Caruyer, Abdol Aziz Ould Ismail, Ronald L. Wolf, Caruyer, Emmanuel, Diffusion & Connectomics In Precision Healthcare Research [Philadelphia, PA, USA] (DiCIPHR), University of Pennsylvania, Center for Biomedical Image Computing & Analytics [Philadelphia] (CBICA), Department of Radiology [Philadelphia], University of Pennsylvania-University of Pennsylvania, Perelman School of Medicine, Synaptive Medical Inc, Departments of Psychiatry and Radiology [Boston], Brigham and Women's Hospital [Boston], Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), R01MH108574, National Institutes of Health, 30071788, Synaptive Medical Inc., University of Pennsylvania [Philadelphia], University of Pennsylvania [Philadelphia]-University of Pennsylvania [Philadelphia], Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1)

Subjects
Male, Computer science, Physiology, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Initialization, Brain Edema, Pathology and Laboratory Medicine, Nervous System, 030218 nuclear medicine & medical imaging, Diagnostic Radiology, 0302 clinical medicine, Medicine and Health Sciences, Edema, Cerebrospinal Fluid, Aged, 80 and over, Brain Mapping, Multidisciplinary, Brain Neoplasms, Radiology and Imaging, Simulation and Modeling, Estimator, Brain, Middle Aged, Magnetic Resonance Imaging, Body Fluids, Diffusion Tensor Imaging, Data Acquisition, Oncology, Medicine, Female, Anatomy, Tractography, Algorithms, Research Article, Adult, Computer and Information Sciences, Adolescent, Imaging Techniques, Science, Brain Morphometry, Brain tumor, Neuroimaging, Research and Analysis Methods, 03 medical and health sciences, Young Adult, Signs and Symptoms, Diagnostic Medicine, medicine, Peritumoral edema, Humans, Aged, business.industry, Biology and Life Sciences, Cancers and Neoplasms, Water, Pattern recognition, medicine.disease, Diffusion Magnetic Resonance Imaging, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Metastatic Tumors, Artificial intelligence, business, 030217 neurology & neurosurgery, Diffusion MRI, Neuroscience
Abstract

International audience; Characterization of healthy versus pathological tissue in the peritumoral area is confounded by the presence of edema, making free water estimation the key concern in modeling tissue microstructure. Most methods that model tissue microstructure are either based on advanced acquisition schemes not readily available in the clinic or are not designed to address the challenge of edema. This underscores the need for a robust free water elimination (FWE) method that estimates free water in pathological tissue but can be used with clinically prevalent single-shell diffusion tensor imaging data. FWE in single-shell data requires the fitting of a bi-compartment model, which is an ill-posed problem. Its solution requires optimization, which relies on an initialization step. We propose a novel initialization approach for FWE, FERNET, which improves the estimation of free water in edematous and infiltrated peritumoral regions, using single-shell diffusion MRI data. The method has been extensively investigated on simulated data and healthy dataset. Additionally, it has been applied to clinically acquired data from brain tumor patients to characterize the peritumoral region and improve tractography in it.

Published
2020
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3. REVIEW: MR elastography of brain tumors

Author

Ralph Sinkus, Katharina Schregel, Alexandra J. Golby, Samuel Patz, Adomas Bunevicius, Laboratoire de Recherche Vasculaire Translationnelle (LVTS (UMR_S_1148 / U1148)), Université Paris 13 (UP13)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), Department of Neurosurgery [Boston], Brigham and Women's Hospital [Boston], Harvard Medical School [Boston] (HMS), University of Göttingen - Georg-August-Universität Göttingen, Physique des ondes pour la médecine, Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Departments of Psychiatry and Radiology [Boston], Université Paris Diderot - Paris 7 (UPD7)-Université Paris 13 (UP13)-Institut National de la Santé et de la Recherche Médicale (INSERM), Ralph, Sinkus, and Georg-August-University = Georg-August-Universität Göttingen

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV]Life Sciences [q-bio], Review Article, Surgical planning, lcsh:RC346-429, 0302 clinical medicine, ComputingMilieux_MISCELLANEOUS, medicine.diagnostic_test, Brain Neoplasms, 05 social sciences, Stiffness, [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], Glioma, Magnetic Resonance Imaging, 3. Good health, medicine.anatomical_structure, Neurology, lcsh:R858-859.7, Elasticity Imaging Techniques, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Elastography, Radiology, medicine.symptom, Meningioma, medicine.medical_specialty, Cognitive Neuroscience, Brain tumor, Neuroimaging, [SDV.CAN]Life Sciences [q-bio]/Cancer, lcsh:Computer applications to medicine. Medical informatics, 050105 experimental psychology, White matter, 03 medical and health sciences, [SDV.CAN] Life Sciences [q-bio]/Cancer, Pituitary adenoma, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, lcsh:Neurology. Diseases of the nervous system, MR elatography, business.industry, medicine.disease, Neurology (clinical), business, 030217 neurology & neurosurgery
Abstract

Highlights • MR elastography allows non-invasive quantification of the shear modulus of tissue. • MRE correlates with intra-operative consistency of meningiomas, pituitary adenomas. • Reported shear modulus values are widely distributed and overlap. • Meningiomas were the stiffest tumor-type relative to normal appearing white matter. • Studies are needed to determine clinical applications of MRE in neuro-oncology., MR elastography allows non-invasive quantification of the shear modulus of tissue, i.e. tissue stiffness and viscosity, information that offers the potential to guide presurgical planning for brain tumor resection. Here, we review brain tumor MRE studies with particular attention to clinical applications. Studies that investigated MRE in patients with intracranial tumors, both malignant and benign as well as primary and metastatic, were queried from the Pubmed/Medline database in August 2018. Reported tumor and normal appearing white matter stiffness values were extracted and compared as a function of tumor histopathological diagnosis and MRE vibration frequencies. Because different studies used different elastography hardware, pulse sequences, reconstruction inversion algorithms, and different symmetry assumptions about the mechanical properties of tissue, effort was directed to ensure that similar quantities were used when making inter-study comparisons. In addition, because different methodologies and processing pipelines will necessarily bias the results, when pooling data from different studies, whenever possible, tumor values were compared with the same subject's contralateral normal appearing white matter to minimize any study-dependent bias. The literature search yielded 10 studies with a total of 184 primary and metastatic brain tumor patients. The group mean tumor stiffness, as measured with MRE, correlated with intra-operatively assessed stiffness of meningiomas and pituitary adenomas. Pooled data analysis showed significant overlap between shear modulus values across brain tumor types. When adjusting for the same patient normal appearing white matter shear modulus values, meningiomas were the stiffest tumor-type. MRE is increasingly being examined for potential in brain tumor imaging and might have value for surgical planning. However, significant overlap of shear modulus values between a number of different tumor types limits applicability of MRE for diagnostic purposes. Thus, further rigorous studies are needed to determine specific clinical applications of MRE for surgical planning, disease monitoring and molecular stratification of brain tumors.

Published
2020
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4. Freewater EstimatoR using iNtErpolated iniTialization (FERNET): Toward Accurate Estimation of Free Water in Peritumoral Region Using Single-Shell Diffusion MRI Data

Author

Wes Hodges, Drew Parker, Simon Alexander, Ronald L. Wolf, Steven Brem, Emmanuel Caruyer, Ofer Pasternak, Ragini Verma, Abdol Aziz Ould Ismail, Moises Hernandez-Fernandez, Center for Biomedical Image Computing & Analytics [Philadelphia] (CBICA), Department of Radiology [Philadelphia], University of Pennsylvania [Philadelphia]-University of Pennsylvania [Philadelphia], University of Pennsylvania [Philadelphia], Department of Neurosurgery [Philadelphia], Synaptive Medical Inc, Departments of Psychiatry and Radiology [Boston], Brigham and Women's Hospital [Boston], Empenn, Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), University of Pennsylvania-University of Pennsylvania, University of Pennsylvania, Neuroimagerie: méthodes et applications (Empenn), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Caruyer, Emmanuel

Subjects
[SDV.BIO]Life Sciences [q-bio]/Biotechnology, Accurate estimation, Computer science, Brain tumor, Initialization, Microstructural modeling, Brain tumors, 030218 nuclear medicine & medical imaging, Diffusion MRI, 03 medical and health sciences, Free water elimination, 0302 clinical medicine, medicine, Edema, [SDV.IB] Life Sciences [q-bio]/Bioengineering, business.industry, Peritumoral region, Estimator, Pattern recognition, medicine.disease, [SDV.BIO] Life Sciences [q-bio]/Biotechnology, 3. Good health, Diffusion tensor imaging, Free water, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Artificial intelligence, business, Tractography, 030217 neurology & neurosurgery
Abstract

Characterization of healthy versus pathological tissue is a key concern when modeling tissue microstructure in the peritumoral area, confounded by the presence of free water (e.g., edema). Most methods that model tissue microstructure are either based on advanced acquisition schemes not readily available in the clinic, or are not designed to address the challenge of edema. This underscores the need for a robust free water elimination (FWE) method that estimates free water in pathological tissue but can be used with clinically prevalent single-shell diffusion tensor imaging data. FWE in single-shell data requires the fitting of a bi-compartment model, which is an ill-posed problem. Its solution requires optimization, which relies on an initialization step. We propose a novel initialization approach for FWE, FERNET, which improves the estimation of free water in edematous and infiltrated peritumoral regions, using single-shell diffusion MRI data. The method has been extensively investigated on simulated data and healthy and brain tumor datasets, demonstrating its applicability on clinically acquired data. Additionally, it has been applied to data from brain tumor patients to demonstrate the improvement in tractography in the peritumoral region.

Published
2019
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5. Characterizing Peritumoral Tissue Using Free Water Elimination in Clinical DTI

Author

Abdol Aziz Ould Ismail, Drew Parker, Moises Hernandez-Fernandez, Steven Brem, Simon Alexander, Ofer Pasternak, Emmanuel Caruyer, Ragini Verma, Section for Biomedical Image Analysis (SBIA), Perelman School of Medicine, University of Pennsylvania [Philadelphia]-University of Pennsylvania [Philadelphia], University of Pennsylvania [Philadelphia], Synaptive Medical Inc, Departments of Psychiatry and Radiology [Boston], Brigham and Women's Hospital [Boston], Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Vision, Action et Gestion d'informations en Santé (VisAGeS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), University of Pennsylvania-University of Pennsylvania, University of Pennsylvania, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Caruyer, Emmanuel

Subjects
[SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

International audience; Finding an accurate microstructural characterization of the peritumoral region is essential to distinguish between edema and infiltration, enabling the distinction between tumor types, and to improve tractography in this region. Characterization of healthy versus pathological tissue is a key concern when modeling tissue microstructure in the peritumoral area, which is muddled by the presence of free water (e.g., edema). Although diffusion MRI (dMRI) is being used to obtain the microstructural characterization of tissue, most methods are based on advanced dMRI acquisition schemes that are infeasible in the clinical environment, which predominantly uses diffusion tensor imaging (DTI), and are mostly for healthy tissue. In this paper, we propose a novel approach for microstructural characterization of peritumoral tissue, that involves multi-compartment modeling and a robust free water elimination (FWE) method to improve the estimation of free water in both healthy and pathological tissue. As FWE requires the fitting of two compartments, it is an ill-posed problem in DTI acquisitions. Solving this problem requires an optimization routine, which in turn relies on an initialization step for finding a solution, which we optimally choose to model the presence of edema and infiltration unlike existing schemes. We have validated the method extensively on simulated data, and applied it to data from brain tumor patients to demonstrate the improvement in tractography in the peritumoral region, which is important for surgical planning.

Published
2018

6. A comparison of three fiber tract delineation methods and their impact on white matter analysis

Author

Amanda E. Lyall, Lauren J. O'Donnell, Ana Maria Rivas-Grajales, Sylvain Bouix, Martha E. Shenton, Fan Zhang, Carl-Fredrik Westin, Sarina Karmacharya, Demian Wassermann, Marek Kubicki, Nikos Makris, Valerie J. Sydnor, Psychiatry Neuroimaging Laboratory (PNL), Brigham and Women's Hospital [Boston], Department of Psychiatry Massachusetts General Hospital (MGH), Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine (BCM), Baylor University-Baylor University, Laboratory of Mathematics in Imaging [Boston], Department of Radiology [Boston], Departments of Psychiatry and Radiology [Boston], Center for Morphometric Analysis (CMA), Massachusetts General Hospital [Boston], Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, European Project: 757672,H2020 Pilier ERC,NeuroLang(2018), College of Information and Electrical Engineering [Beijing] ( CIEE ), China Agricultural University ( CAU ), Psychiatry Neuroimaging Laboratory ( PNL ), Center for Morphometric Analysis ( CMA ), Massachusetts General Hospital [Boston] ( MGH ), Computational Imaging of the Central Nervous System ( ATHENA ), Inria Sophia Antipolis - Méditerranée ( CRISAM ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Inria Saclay - Ile de France, Brogham Women's Hospital, European Project : 757672,NeuroLang, Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
Male, Computer science, Cognitive Neuroscience, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Fiber tract, Uncinate fasciculus, Brain mapping, Article, 030218 nuclear medicine & medical imaging, Diffusion MRI, White matter, 03 medical and health sciences, 0302 clinical medicine, [ PHYS.PHYS.PHYS-BIO-PH ] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Region of interest, Fractional anisotropy, Fasciculus, Image Processing, Computer-Assisted, medicine, Humans, Arcuate fasciculus, ComputingMilieux_MISCELLANEOUS, Brain Mapping, medicine.diagnostic_test, biology, Automatic classification of white matter tracts, Brain, Magnetic resonance imaging, biology.organism_classification, Diffusion Tensor Imaging, medicine.anatomical_structure, Neurology, Cartography, Tractography, 030217 neurology & neurosurgery
Abstract

Diffusion magnetic resonance imaging (dMRI) is an important method for studying white matter connectivity in the brain in vivo in both healthy and clinical populations. Improvements in dMRI tractography algorithms, which reconstruct macroscopic three-dimensional white matter fiber pathways, have allowed for methodological advances in the study of white matter; however, insufficient attention has been paid to comparing post-tractography methods that extract white matter fiber tracts of interest from whole-brain tractography. Here we conduct a comparison of three representative and conceptually distinct approaches to fiber tract delineation: 1) a manual multiple region of interest-based approach, 2) an atlas-based approach, and 3) a groupwise fiber clustering approach, by employing methods that exemplify these approaches to delineate the arcuate fasciculus, the middle longitudinal fasciculus, and the uncinate fasciculus in 10 healthy male subjects. We enable qualitative comparisons across methods, conduct quantitative evaluations of tract volume, tract length, mean fractional anisotropy, and true positive and true negative rates, and report measures of intra-method and inter-method agreement. We discuss methodological similarities and differences between the three approaches and the major advantages and drawbacks of each, and review research and clinical contexts for which each method may be most apposite. Emphasis is given to the means by which different white matter fiber tract delineation approaches may systematically produce variable results, despite utilizing the same input tractography and reliance on similar anatomical knowledge.

Published
2018
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7. Mathematical Models for Diffusion MRI Processing

Author

Lenglet, C., Campbell, J. S. W., Descoteaux, Maxime, Haro, G., Savadjiev, P., Wassermann, Demian, Anwander, Alfred, Deriche, Rachid, Pike, G Bruce, Sapiro, Guillermo, Siddiqi, K., Thompson, P., Department of Electrical and Computer Engineering [Minneapolis] (ECE), University of Minnesota [Twin Cities] (UMN), University of Minnesota System-University of Minnesota System, Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota Medical School, McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Center for Intelligent Machines [Montréal], McGill University = Université McGill [Montréal, Canada], Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Departament de Tecnologies de la Informació i les Comunicacions [Barcelona] (ETIC / UPF), Universitat Pompeu Fabra [Barcelona] (UPF), Departments of Psychiatry and Radiology [Boston], Brigham and Women's Hospital [Boston], Computer and biological vision (ODYSSEE), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-École des Ponts ParisTech (ENPC), Department of Computer Science [Buenos Aires], Universitad de Buenos Aires = University of Buenos Aires [Argentina], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Laboratory of Neuro Imaging [Los Angeles] (LONI), University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), University of Buenos Aires, University of California-University of California, Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
[INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Abstract

Special Issue on Mathematics in Brain Imaging. In Press, Accepted Manuscript, Available online 13 November 2008 doi:10.1016/j.neuroimage.2008.10.054; International audience; In this article, we review recent mathematical models and computational methods for the processing of diffusion Magnetic Resonance Images, including state-of-the-art reconstruction of diffusion models, cerebral white matter connectivity analysis, and segmentation techniques. We focus on Diffusion Tensor Images (DTI) and Q-Ball Images (QBI).

Published
2008
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8. fNIRS brain measures of ongoing nociception during surgical incisions under anesthesia.

Author

Green S, Karunakaran KD, Labadie R, Kussman B, Mizrahi-Arnaud A, Morad AG, Berry D, Zurakowski D, Micheli L, Peng K, and Borsook D

Abstract

Significance : Functional near-infrared spectroscopy (fNIRS) has evaluated pain in awake and anesthetized states. Aim : We evaluated fNIRS signals under general anesthesia in patients undergoing knee surgery for anterior cruciate ligament repair. Approach : Patients were split into groups: those with regional nerve block (NB) and those without (non-NB). Continuous fNIRS measures came from three regions: the primary somatosensory cortex (S1), known to be involved in evaluation of nociception, the lateral prefrontal cortex (BA9), and the polar frontal cortex (BA10), both involved in higher cortical functions (such as cognition and emotion). Results : Our results show three significant differences in fNIRS signals to incision procedures between groups: (1) NB compared with non-NB was associated with a greater net positive hemodynamic response to pain procedures in S1; (2) dynamic correlation between the prefrontal cortex (PreFC) and S1 within 1 min of painful procedures are anticorrelated in NB while positively correlated in non-NB; and (3) hemodynamic measures of activation were similar at two separate time points during surgery (i.e., first and last incisions) in PreFC and S1 but showed significant differences in their overlap. Comparing pain levels immediately after surgery and during discharge from postoperative care revealed no significant differences in the pain levels between NB and non-NB. Conclusion : Our data suggest multiple pain events that occur during surgery using devised algorithms could potentially give a measure of "pain load." This may allow for evaluation of central sensitization (i.e., a heightened state of the nervous system where noxious and non-noxious stimuli is perceived as painful) to postoperative pain levels and the resulting analgesic consumption. This evaluation could potentially predict postsurgical chronic neuropathic pain., (© 2022 The Authors.)

Published
2022
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9. Characteristics and Outcome in Children With Craniectomy Following Acute Ischemic Stroke in the International Pediatric Stroke Study.

Author

Lehman LL, DeVeber G, Pergami P, Guilliams KP, Chung M, Felling R, Dowling MM, and Rivkin MJ

Subjects
Adolescent, Child, Child, Preschool, Female, Humans, Infant, Infant, Newborn, Male, Registries, Retrospective Studies, Treatment Outcome, Brain Ischemia surgery, Decompressive Craniectomy, Stroke surgery
Abstract

The outcome of children with acute ischemic stroke treated with craniectomy has not been thoroughly examined. In adults, hemicraniectomy after middle cerebral artery territory stroke and posterior decompression after posterior circulation stroke has been shown to improve outcome. Pediatric cases of hemicraniectomy for middle cerebral artery stroke and posterior decompression following posterior circulation stroke suggest relatively good outcome. There are no published data in adults or children with craniectomy after cerebral sinovenous thrombosis. Our aim was to determine the outcome of children with acute ischemic stroke treated with craniectomy in the International Pediatric Stroke Study (IPSS). We included children enrolled who had a craniectomy following stroke presentation. Of 4294 patients in IPSS, 38 children (1%) were found to have craniectomy following an ischemic stroke. Of 38 craniectomy cases, 29 had anterior circulation strokes, 5 had posterior circulation strokes, and 4 had cerebral sinovenous thromboses. The mortality rate was 8%. Overall, children who have craniectomies have significant neurologic deficits. Prospective studies are needed to examine long-term morbidity following craniectomy.

Published
2019
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10. Comparing free water imaging and magnetization transfer measurements in schizophrenia.

Author

Mandl RC, Pasternak O, Cahn W, Kubicki M, Kahn RS, Shenton ME, and Hulshoff Pol HE

Subjects
Adolescent, Adult, Anisotropy, Brain Mapping, Female, Humans, Male, Middle Aged, White Matter metabolism, White Matter pathology, Young Adult, Brain metabolism, Diffusion Magnetic Resonance Imaging, Magnetic Resonance Imaging, Schizophrenia pathology, Water metabolism
Abstract

Diffusion weighted imaging (DWI) has been extensively used to study the microarchitecture of white matter in schizophrenia. However, popular DWI-derived measures such as fractional anisotropy (FA) may be sensitive to many types of pathologies, and thus the interpretation of reported differences in these measures remains difficult. Combining DWI with magnetization transfer ratio (MTR) - a putative measure of white matter myelination - can help us reveal the underlying mechanisms. Previous findings hypothesized that MTR differences in schizophrenia are associated with free water concentrations, which also affect the DWIs. In this study we use a recently proposed DWI-derived method called free-water imaging to assess this hypothesis. We have reanalyzed data from a previous study by using a fiber-based analysis of free-water imaging, providing a free-water fraction, as well as mean diffusivity and FA corrected for free-water, in addition to MTR along twelve major white matter fiber bundles in 40 schizophrenia patients and 40 healthy controls. We tested for group differences in each fiber bundle and for each measure separately and computed correlations between the MTR and the DWI-derived measures separately for both groups. Significant higher average MTR values in patients were found for the right uncinate fasciculus, the right arcuate fasciculus and the right inferior-frontal occipital fasciculus. No significant results were found for the other measures. No significant differences in correlations were found between MTR and the DWI-derived measures. The results suggest that MTR and free-water imaging measures can be considered complementary, promoting the acquisition of MTR in addition to DWI to identify group differences, as well as to better understand the underlying mechanisms in schizophrenia., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Published
2015
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